基于互相关熵的非高斯背景下微动参数估计方法

熊丁丁; 崔国龙; 孔令讲; 杨晓波

doi:10.12000/JR17007

基于互相关熵的非高斯背景下微动参数估计方法

doi: 10.12000/JR17007

电子科技大学电子工程学院成都 611731

基金项目: 国家自然科学基金(61501083, 61301266)

详细信息

作者简介:
熊丁丁(1993–)，女，江西人，电子科技大学硕士研究生，研究方向为微动目标检测和估计、手势雷达定位和识别等。 E-mail: 862815090@qq.com

崔国龙(1982–)，男，安徽人，电子科技大学副教授，博士生导师，《雷达学报》编委。研究方向为最优化理论和算法、雷达目标检测理论、波形多样性以及阵列信号处理等

孔令讲(1974–)，男，河南人，电子科技大学教授，博士生导师，长江学者。研究方向为宽带雷达系统技术、弱目标检测跟踪技术、雷达协同探测技术、相控阵激光雷达技术等

杨晓波(1964–)，男，四川人，电子科技大学教授，博士生导师。研究方向为穿墙雷达技术、弱目标检测跟踪技术、雷达协同探测技术等

通讯作者:
崔国龙　 cuiguolong@uestc.edu.cn

中图分类号: TN957.51
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出版历程
- 收稿日期: 2017-01-17
- 修回日期: 2017-03-09
- 网络出版日期: 2017-06-28

Micro-motion Parameter Estimation in Non-Gaussian Noise via Mutual Correntropy

School of Electronics Engineering, University of Electronic Science and Technology, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (61501083, 61301266)

摘要

摘要: 针对非高斯背景下微动目标的参数估计问题，该文采用单发多收(SIMO)的线性调频连续波(LFMCW)雷达系统，提出了一种基于互相关熵的微动参数估计方法。该方法利用多通道回波信号的2阶和高阶信息，对回波中所含的目标信息实现更准确的量化，从而得到更好的微动参数估计效果。在非高斯背景下，相比传统傅里叶变换的方法，该方法能在微动目标的成像效果中实现更好的雷达成像效果以及更高的输出信噪比。同时，该文采用单脉冲比相(PCM)定位的方法，通过提取多通道回波的相位信息，计算不同通道间的相位差和目标的方位角，从而实现了对微动目标的准确定位。最后，仿真结果证明了该方法的有效性。
- 微动目标 /
- 单发多收 /
- 非高斯背景 /
- 互相关熵
Abstract: This study considered parameter estimations for micro-motion targets embedded in non-Gaussian noise with a Single Input Multiple Output (SIMO) radar. A novel estimation algorithm based on mutual correntropy was presented and used to derive the micro-perturbation parameters by exploiting the second and higher-order knowledge of the return signals among multiple channels. Compared with a conventional Fourier Transform (FT) method, the method proposed herein had a much higher Signal to Noise Ratio (SNR) gain. In addition, the location was derived by employing the Phase-Comparison Monopulse (PCM) technique. Finally, several numerical results were provided and discussed.
- Micro-motion /
- Single Input Multiple Output (SIMO) /
- Non-Gaussian noise /
- Mutual correntropy

HTML全文

图 1 SIMO雷达模型

Figure 1. The SIMO system model

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图 2 LFMCW时频关系

Figure 2. Frequency-time relation of LFMCW

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图 3 算法流程图

Figure 3. The flowchart of the mutual correntropy algorithm

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图 4 PCM测角示意图

Figure 4. The schematic of PCM algorithm

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图 5 传统傅里叶分析结果

Figure 5. Conventional Fourier analysis results

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图 6 自相关熵分析结果

Figure 6. The auto-correntropy processing results

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图 7 互相关熵分析结果

Figure 7. The mutual correntropy processing results

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图 8 非高斯背景下输入输出信噪比关系

Figure 8. The output SNR versus different input SNR

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图 9 微动目标定位结果

Figure 9. The location result of PCM

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表 1 仿真参数

Table 1. Simulation parameters

参数	探测参数	参数值
	载波频率f₀	1 GHz
	信号带宽B	500 M
系统参数	信号时宽T	3.31 ms
	LFM周期数N	512
	核尺度 $\sigma $	0.2
目标参数	距离R	6 m
	振动频率f₁	4 Hz
	振动频率f₂	8 Hz
	振动幅度A₁	4 mm
	振动幅度A₂	4 mm
	坐标(x, y)	(0, 6) m
	信噪比SNR_in	–10 dB
场景噪声参数	噪声类型	韦布尔噪声
	韦布尔形状因子P	2

下载: 导出CSV

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